The field of the invention is systems and methods for magnetic resonance imaging (“MRI”). More particularly, the invention relates to systems and methods for combining k-space data acquired from multiple different receiver channels during image reconstruction.
Over the past decade, multichannel receiver arrays have revolutionized MRI by improving the achievable signal-to-noise ratio (“SNR”) and providing spatial encoding information complimentary to traditional gradient encoding. These technical advantages have been leveraged across a variety of applications to reduce scan time, improve spatial and/or temporal resolution, increase coverage, or reduce certain types of artifacts, such as those associated with motion and geometric distortion.
Even as multichannel receiver technology has been a clear success story, it is not without cost. For instance, multi-element anatomy-specific coil arrays are expensive to develop and manufacture, and a greater burden is placed on image reconstruction systems, which can struggle to process the increased amount of data within acceptable latency times for clinical applications.
Maintaining acceptable reconstruction latency can be challenging when using a high number of receiver channels. For real-time imaging, images must be acquired and reconstructed with minimal latency, which has led to recent interest in k-space channel combination using rapid non-Cartesian acquisitions. Channel combination of k-space data reduces reconstruction latency because memory requirements are reduced such that even large multichannel 3D datasets can be gridded during data acquisition, channel combination can take place during image acquisition, and the number of Fourier transforms required to transform the data to image space is reduced from the number of channels to one.
It would therefore be desirable to provide a system and method for lessening the computational burden required to reconstruct images from multichannel data. It would also be desirable to achieve this results for data acquired using non-Cartesian k-space trajectories. While the vast majority of clinical MRI is currently performed using Cartesian k-space trajectories, non-Cartesian MRI can be advantageous for applications such as ultra-fast imaging, motion correction, ultra-short echo time imaging, and certain types of dynamic imaging.
Reconstruction of multichannel non-Cartesian data sets is typically performed by reconstructing a separate image for each acquisition channel, followed by combining these images to form a single composite image. For example, a gridding reconstruction can be applied to reconstruct separate channel images followed by a sum-of-squares channel combination.
In 2011, the feasibility of combining “direct virtual coil” (DVC) technology with a gridding reconstruction to enable multi-channel data to be gridded (deposited) onto a single k-space grid was demonstrated by P. J. Beatty, et al., in “k-Space Channel Combination for Non-Cartesian Acquisitions Using Direct Virtual Coil (DVC) Calibration,” Proc. of ISMRM, 2011; 2858. This technique effectively combined multichannel data in k-space earlier in the reconstruction process. FIG. 1 illustrates an example of this previous approach for combining multichannel k-space data.
A standard Kaiser-Bessel gridding kernel 106 is generated with a kernel width of 4 and an oversampling ratio of 1.5., and a DVC kernel 104 is generated, also for an oversampling ratio of 1.5. Because the discrete samples of these two kernels align on the same 1.5× grid, they can be discretely convolved to form a final convolution kernel 102. The Kaiser-Bessel kernel is applied directly to the k-space acquisition position and sampled according to the final grid spacing. This dictates that the DVC kernel must also be created with final grid spacing. When the two kernels are convolved together, the result is a larger kernel (width 8 in this case). As illustrated in FIG. 2, in the approach proposed in 2011, the purpose of the DVC filter 202 (Fourier Transform of the DVC kernel) is to create a pass band 204 that mimics the conjugate of the coil sensitivity. The Kaiser-Bessel filter 206 is then responsible for suppressing content in the stopbands 208, just as in traditional gridding.
This DVC approach significantly reduces the amount of computer memory required for data combination and reconstruction. For instance, rather than requiring a grid matrix for every channel, only one grid matrix is required, regardless of channel count. This advantage is especially notable for large 3D acquisitions, where a grid matrix for all channels cannot be simultaneously stored in RAM memory; in this case, the reconstruction latency is greatly increased when reconstructing separate channel images. Additionally, only one grid matrix needs to be Fourier transformed and all subsequent reconstruction processes take place on only a single channel matrix, thereby reducing the required computation.
Notwithstanding these advantages, there is a problem with the approach proposed in 2011. In particular, the approach results in a larger convolution kernel compared to the traditional gridding approach. Specifically, for 2D image reconstructions, an 8×8 convolution kernel was required by the DVC approach to achieve image quality comparable to a 4×4 kernel used with traditional gridding. The additional computation required by the larger convolution kernel can significantly add to the time and computational burden for reconstruction.